A key challenge in profiling users' behavior on the Web is to know which content to recommend to a user: Domain recommendations, where the user is directed to the homepage of a given site, while somewhat effective, have traditionally been severely limited. For example, a site may contain many sub sections of widely varying content and hence of interest to widely different groups. This site is fairly typical of a large internet Web site with many magazine and news services having similar breadth of content and structure.
One conventional solution provided by search engines allows the user to explicitly specify his topic of interest in his query, which allows the search engine to focus on the relevant part of the web site. Further enhancements of this approach allow for content so discovered to be rated according to its likely utility e.g. Google™ Page Rank, Google™ Site Links, etc. While effective, it unfortunately requires the user to explicitly state his information goal. Another approach has been to offer the most popular subsections on this site as a form of recommendation or the most recently popular buzz sections within the site. While again effective, this approach is limited to what is popular and requires the user to go to the site's home page or other intervening page to see these recommendations. Yet another approach uses Really Simple Syndication (RSS) feeds to keep users appraised of current items of interest to them in a given site or section thereof. Again this approach requires the user to manually identify and subscribe to different feeds.
There is thus a need for addressing these and/or other issues associated with the prior art.